The Future of Reading Faces

An interesting blog post in the Wall Street Journal a couple weeks ago highlighted a startup company called MedNetworks that, among other things, analyzes social networking data to help pharmaceutical salespeople target their pitches to influential doctors. Founded by leading social network researcher Nicholas Christakis, MedNetworks aims to leverage the growing body of research that shows how ideas and behaviors spread subconsciously.

According to the Journal, the company has already had some success:

But the technology can also be applied to helping pharma companies pinpoint the physicians on whom to concentrate their marketing efforts during a time of sales-force downsizing. Concentrating only on high-volume prescribers “completely misses the social context,” says Miller. It can’t find the doctors who may not prescribe the most themselves, but know so many co-workers and colleagues in the area — say, because they refer out a lot of patients –that they can influence prescription volume downstream.

A case study on the launch of Merck’s diabetes drug Januvia, for example, showed that in the Raleigh-Durham area,”prescribers who had Januvia adopters within one degree of separation in their network neighborhood were twice as likely to prescribe Januvia compared to prescribers without Januvia adopters in their network neighborhood.” Other regions showed similar patterns, the company says.

I suppose this isn't too shocking--companies are already measuringhow quickly people returns your phone calls, among other things, to develop a rough sense of social hierarchy, and then tailor sales pitches, so I guess prescription habits aren't too different. And from a business perspective, it certainly makes sense. Why employ a salesforce when you can just monetize friendship instead?

It's intriguing, though also scary, to think about how this sort of marketing could evolve in combination with technologies like facial recognition software, a technology that we're already starting to see deployed in retail contexts. For example, this Japanese vending machine can determine your sex and approximate age, by your face, and use this information to make recommendations:

"Recommended" labels will then appear on specific drink products. Suggested products may also change depending on the temperature and time of day.

"If the customer is a man, the machine is likely to recommend a canned coffee drink, since men tend to prefer these. If the customer is in their 50s, though, that recommendation is likely to be green tea," a company spokeswoman said.

A woman in her 20s will be recommended a tea drink or slightly sweeter product, since market research has shown that they prefer these.

This is obviously pretty crude right now, but as facial recognition software improves, it's easy to imagine tying in-store facial recognition to data about our position in different social networks. In-store, companies could begin tailoring everything from pitches to service levels to you, just by looking at your face.